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L. F. V. Scharff, Albert J. Ahumada, Jr.; Predicting the readability of transparent text. Journal of Vision 2002;2(9):7. doi: 10.1167/2.9.7.
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© ARVO (1962-2015); The Authors (2016-present)
Will a simple global masking model based on image detection be successful at predicting the readability of transparent text? Text readability was measured for two types of transparent text: additive (as occurs in head-up displays) and multiplicative (which occurs in see-through liquid crystal display virtual reality displays). Text contrast and background texture were manipulated. Data from two previous experiments were also included (one using very low contrasts on plain backgrounds, and the other using higher-contrast opaque text on both plain and textured backgrounds). All variables influenced readability in at least an interactive manner. When there were background textures, the global masking index (that combines text contrast and background root mean square contrast) was a good predictor of search times (r = 0.89). When the masking was adjusted to include the text pixels as well as the background pixels in computations of mean luminance and contrast variability, predictability improved further (r = 0.91).
Negative numbers indicate that the text was darker than the background. Text contrast values for each text type do not depend on the background pattern.
Negative numbers indicate that the text was darker than the background. Unlike the adjusted text contrast values, the adjusted background root mean square (RMS) values are affected by both the background pattern and the text contrast.
Shown are search times for all experiments separately and the combined data for both the original and the adjusted global masking index using pT = 0.20. The adjusted global masking index leads to the best predictability of readability.
MS=mean squares; df=degrees of freedom; MSE=mean squares error.
RMS=root mean square.
Conditions had five levels (0.15, 0.35, and 0.95 text contrast on a plain background, and 0.35 and 0.95 contrast on the culture background). MS=mean squares; df=degrees of freedom; MSE=mean squares error.
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